In a nutshell: Once we start thinking in terms of pedagogical patterns, we are handed the keys to Blended Learning. We can make informed decision about which parts of our modules to design online and which ones face-to-face. This is an introductory session that demonstrates how the world’s most complex pedagogical philosophies, such as Problem-based Learning (Barrows, 1992, 1996) and Design Thinking (Hasso Plattner Institute of Design, 2020; Stanfordonline, 2016, 2018) can be conceptualized within the pedagogical patterns of Laurillard’s Conversational Framework (Video).
The purpose of this posting is to motivate educators to start envisioning future education in terms of pedagogical patterns. Thinking in pedagogical patterns is essential to planning relevant and effective learning processes, in particular implementing pedagogical strategies within technological environments. The presented analysis is based on the Conversational Framework by Diana Laurillard (2012). The topic of this posting is not the ABC Workshop but instead, it is an exercise in demonstrating how different pedagogical patterns support different types of learning outcomes. It is a warming-up exercise for educators who have just begun embarking on digital education.
About pedagogical patterns and pedagogical algorithms
Pedagogical patterns are represented by learning activities. If how we learn is not aligned to what we learn, learning itself becomes confusing, ambiguous, ineffective and even counter-productive. Learners construct meaning from what and how they learn. Therefore, learning activities need to be aligned with assessment methods and outcomes (Biggs & Tang, 2011).
For example, it would make little sense to teach critical thinking, followed by a multiple-choice test, or to teach conversation in a foreign language class followed by a written vocabulary test when the stipulated learning outcome to ability to freely communicate. As in science, the measurement (the evaluation of characteristics and instances of a phenomenon) must relate to the construct it claims to measure. Otherwise, measurements are neither reliable nor valid.
The advantage of the approach to thinking in terms of learning activities is not only the reconstruction of pedagogical logic but it allows examining which learning activities could be conducted online and which learning phases face-to-face. The ability to suggest sensible learning activities is a prerequisite to planning Blended Learning modules.
Such a ‚Open Source Pedagogy‘ allows for systematic innovation and improvement of educational programs and modules since collaborating educators can tweak the sequence of learning activities while monitoring desired outcomes. Last, but not least, the approach enables the planning of learning analytics to support and foster student learning, which will be covered in the latter part of this series.
The notion of ‚pedagogical algorithms‘, borrowing a term from computer science, might be puzzling for some readers. In our context, it means, that a specific sequence of learning activities, defined in the formal language of the Conversational Framework, produces a desired nature of general pedagogical outcomes. For example, if the goal is to solve problems, a sequence of learning activities supporting problem-solving should be applied. If the goal is to create novel solutions, then a sequence of learning activities developing creativity and the generation of diverse ideas should be engaged. If the goal is the improvement of skills, a sequence of learning activities involving structured practice and coaching would be the preferred choice. If the goal is to develop transfer skills, student could work on a variety of case studies – and so on and so forth.
In all of these cases, teaching is understood as a design science. Teaching and learning is not an arbitrary assembly of ideas and methods. Pedagogical algorithms differ fundamentally from computer algorithms: human nature involves learning by reflected action, by creating meaning, by building learning communities and cherished relationships, by rich interpretations of reality, by emotional and metacognitive learning experiences, by critically questioning underlying goals, assumptions and social constructs. Pedagogical algorithms are similar to computer algorithms insofar they employ a logic of learning activities guiding students towards general and specific learning outcomes.
I suggest the following preliminary definitions:
Pedagogical patterns are strings of learning activities that follow logical and connected progressions (such as, e.g., discussing a theory after researching it or formulating hypotheses after a literature review) as described in the Conversational Framework (Laurillard, 2012). The goal of pedagogical patterns is to create holistic learning experiences.
Pedagogical algorithms are pedagogical patterns that support defined general learning outcomes. General learning outcomes are open in nature and can be freely defined, such as e.g., developing analytical skills, problem-solving skills, transfer skills, methods to explore creative solutions etc.
Here is an example on patterns (Specific Learning Outcomes by activities/ SLOs) and algorithms (General Learning Outcomes by categories of learning/ GLOs):
Learning in the 21st Century: Rediscovering what makes us human
Students need to develop competencies that cannot be automated. The entire idea of making sequences of pedagogical patterns transparent is to promote areas of the human excellence, such as critical and informed discussion, the understanding new contexts, reflection, collaboration, goal-directed enquiry, empathic identification or structured collaboration. Rote learning and the acquisition of low-level cognitive skills, by comparison, force young people, sooner or later, to be replaced by machine learning systems and AI. It is our responsibility as educators to create more demanding social spaces to facilitate higher levels of learning. The video below demonstrates how sequences of learning activities (as defined in the Conversational Framework) are embedded in the most complex of educational philosophies (in this case, Problem-based Learning and Design Thinking). Design Thinking shows some similarity to Grounded Theory since data collection, the saturation of categories and data analysis are conducted as synthesizing and simultaneous processes. They do not work in a traditional analytical and deductive manner.
The next part of the series elaborates on how to transform existing, older curricula into Blended Learning programs. For educators who prefer to proceed directly into curriculum development, I highly recommend the above-mentioned ABC workshop and hope that the video below encourages educators to experiment with the effects of varying pedagogical patterns and algorithms.
Recommended video and prerequisite: Introduction to the Conversational Framework and the six learning types by Prof. Laurillard:
Barrows, H. S. (1992). The tutorial process. Springfield, Ill: Southern Illinois University School of Medicine.
Barrows, H. S. (1996), Problem-based learning in medicine and beyond: A brief overview. New Directions for Teaching and Learning, 1996: 3–12. doi:10.1002/tl.37219966804
Biggs, J. B., & Tang, C. S. (2011). Teaching for quality learning at university: What the student does. Maidenhead, England
Hasso Plattner Institute of Design (2020). Design Thinking Bootleg:. Retrieved from https://dschool.stanford.edu/resources/design-thinking-bootleg
Laurillard, D. (2012). Teaching as a Design Science: Building Pedagogical Patterns for Learning and Technology. Routledge. New York and London
Stanfordonline (2016) Design Thinking = Method, Not Magic [Video]. Retrieved from: https://www.youtube.com/watch?v=vSuK2C89yjA
Stanfordonline (2018) Creating a Culture of Creativity: Conquering Fear and the Internal Censor. Retrieved from: https://www.youtube.com/watch?v=j35R_X5hLhQ&feature=youtu.be